Spherical-harmonic decomposition for molecular recognition in electron-density maps

Authors:
Frank P. DiMaio;Ameet B. Soni;George N. Phillips;Jude W. Shavlik
Affiliations:
Departments of Computer Sciences and Biostatistics and Medical Informatics, University of Wisconsin, 1210 W. Dayton St., Madison, WI, USA.;Departments of Computer Sciences and Biostatistics and Medical Informatics, University of Wisconsin, 1210 W. Dayton St., Madison, WI, USA.;Departments of Biochemistry and Computer Sciences, University of Wisconsin, 433 Babcock Dr., Madison, WI, USA.;Departments of Computer Sciences and Biostatistics and Medical Informatics, University of Wisconsin, 1210 W. Dayton St., Madison, Madison, WI, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2009

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Abstract

Several methods for automatically constructing a protein model from an electron-density map require searching for many small protein-fragment templates in the density. We propose to use the spherical-harmonic decomposition of the template and the maps density to speed this matching. Unlike other template-matching approaches, this allows us to eliminate large portions of the map unlikely to match any templates. We train several first-pass filters for this elimination task. We show our new template-matching method improves accuracy and reduces running time, compared to previous approaches. Finally, we extend our method to produce a structural-homology detection algorithm using electron density.